Understanding similarities between data is at the heart of many machine learning tasks such as data retrieval [1]}, [2]}, [3]}, [4]}, face verification [5]}, [6]}, person re-identification [7]}, [8]}, few-shot learning [9]}, [10]}, [11]}, and representation learning [1]}, [13]}, [14]}.
Metric learning embodies the perception of similarity by learning an embedding space where the distance between a pair of data represents their inverse semantic similarity.
Also, the mapping from data to such a space is typically modeled by deep neural networks.
